analytics tracks (sap inside track noida ( delhi – gurgaon) 26 july 2014

Saurabh Thukral│ Product Manager, SAP BI

SAP Advanced AnalyticsDemocratizing the use of Predictive Analytics

© 2014 SAP AG or an SAP affiliate company. All rights reserved. 2Customer

2

HOW DO YOU ACCELERATE YOUR GROWTH?HOW DO YOU ACCELERATE YOUR GROWTH?

CLOUDCLOUD MOBILEMOBILE

An emerging middle class growing to 5B

Data doubling every

18 months

More mobile devices

than people

1 billion people on Facebook

15 billion web-enabled

devices in 2013

THINGSTHINGSDATADATA

In a world of accelerated change…


Analytics Maturity

Sense & Respond Predict & Act

RawData

CleanedData

Standard Reports

Ad Hoc Reports &

OLAP

Generic Predictive Analytics

Predictive Modeling

Optimization

What happened?

Why did it happen?

What will happen?

What is the best that could happen?

Com

petit

ive A

dva

nta

ge

Analytics Maturity

The key is unlocking data to move decision making from sense & respond to predict & act


What is Predictive Analytics?

Predictive Analytics is quantitative analysis to support predictions. Examples include: forecasting of product sales, costs, metrics; analyzing customer churn; credit scoring, identifying cross sell / up sell

opportunities, measuring market campaign response; anomaly detection and fraud detection etc.

It comprises primarily of Statistical Analysis and Data Mining, but can also include methods and techniques from Operations Research.


ChallengesChallenges

Forecasting

KeyInfluencers

Trends

Anomalies

Relationships

How do historical sales, costs, key performance metrics, and so on, translate to future performance? How do predicted results compare with goals?

What are the main influencers of customer satisfaction, customer churn, employee turnover, and so on, that impact success?

What are the trends: historical / emerging, sudden step changes, unusual numeric values that impact the business?

What are the correlations in the data? What are the

cross-sell and up-sell opportunities?

What anomalies might exist and conversely

what groupings or clusters might exist for

specific analysis?

Where Predictive Analytics is used

Imagine the Business Potential…Predictive Use Cases – Industry & LoB

• Customer Churn / Retention

• Cross-Sell / Upsell• Campaign

Management

•Lifetime Value•Pricing Optimization•Product Launch Success•Brand Sentiment & Sales Analytics•Cross/Up Sell

•Product Launch Success•Brand Sentiment & Sales Analytics

•Regional Forecasting•Brand Sentiment & Sales Analytics

•Next Best Activity•Cross Sell/Upsell•Churn ReductionCustomer SegmentationBrand Sentiment & Sales Analytics

•Brand Sentiment & Sales Analytics

• Credit Risk• Fraud Management

& Prevention

•Credit Scoring•Fraud Management & Prevention•Optimizing Product Quality

•Credit Scoring•Compliance•Retail Outlier•Fraud Management & Prevention•Optimizing Product Quality

•Credit Scoring•Compliance•Fraud Management & Prevention•Optimizing Product Quality

•Credit Scoring•Underwriting•Default/bankruptcy risk•Tax Fraud•Credit Card Fraud•Insurance Fraud

•Predictive Asset Maintenance•Fraud Management & Prevention•Optimizing Product Quality

•Anomaly detection•Usage forecasting•Customer Segmentation

•KPI Forecasting•Anomaly detection•Usage forecasting•Store Segmentation•In-store Workforce Optimization•Size and Zone Optimization•Market Share Prediction

•KPI Forecasting•Anomaly detection•Usage forecasting



•KPI Forecasting•Anomaly detection•Usage forecasting•Variable Margin Analysis•Yield Management•Equipment Effectiveness•Labor Utilization

•Out of Stock Prediction•Demand Forecasting•Inventory and Logistics Planning

•Out of Stock Prediction•Inventory and Logistics Planning

•Out of Stock Prediction•Inventory and Logistics Planning

•Predictive Commodity Management•Improving Demand Planning and Inventory Management

Retail CPG Financial Services ManufacturingTelecom E-Business

Customer /Marketing

Fraud/Risk

Operations

SupplyChain


Acquire

RetainCross & Up-Sell

Your Customer

Optimize every customer interaction

Challenging to detect meaningful signals in big data

Severe analytics skills shortage 50-60% shortfall for

experienced data analystsDun & Bradstreet and McKinsey Global Institute analysis

86% of organizations that

used predictive realized a competitive advantage

Sense and respond are no longer enough

Difficult to apply predictive algorithms to anticipate business trends


Challenges and Inefficiencies

Analysts: Talent Shortage

Fragmented Point Solutions

Usability Shortcomings

Lack of VisualizationModel ProliferationHigh Latency

Operational Datastore

Sensors Mobile ArchivesSocial & Text

Order Processing Operational Reporting

RT Risk & Fraud Trend Analysis Sentiment Analytics

Predictive Analytics

PatternRecognition

Spatial Processing

Analyze

Data Stores

Integrate/Load

Staging

Collect

Clean-DataQuality

Transact

Report

Explore

Communicate

Monitor

Predict

Planning

0

1

DataWarehouse

Geo-Spatial

Cache Cache Cache Cache CacheCache

Business & IT: Segregated Organization Structure

Lack of Decision Support

Lack of Data Governance

Complex Slow Costly

Advanced Analytics – SAP Vision

Operationalize predictive & optimization models across the enterprise

Reduce Decision Latency with Advanced Analytics

Bringing Predictive Analytics to a broad spectrum of users

Embed Smart Agile Analytics into Decision Processes to Deliver Business Impact

Easy Fast Efficient

SAP Solutions for the Entire Spectrum of Users

Business Users & LOBDataScientist

Business Analysts

Level Of Skill Set - Analytics

Low HighNo

97% 3% >0.1%Embedded AnalyticsIndustry & Business Process Analytics

CustomAnalytics

SAP Lumira SAP InfiniteInsight (KXEN) SAP Predictive Analysis SAP PAL R Integration

SAP ADVANCED ANALYTICS


The Forrester Wave™Big Data Predictive Analytics Solutions, Q3 2014

A leader in predictive

“SAP is a Leader in predictive analytics due to a strong architecture and strategy.”

Comprehensive and holistic approach to “Big Data”

“SAP also differentiates by putting its SAP HANA in-memory appliance at the center of its offering, including an in-database predictive analytics library (PAL), and offering a modeling tool in SAP Predictive Analysis.”

The Forrester Wave™ is copyrighted by Forrester Research, Inc. Forrester and Forrester Wave™ are trademarks of Forrester Research, Inc. The Forrester Wave™ is a graphical representation of Forrester's call on a market and is plotted using a detailed spreadsheet with exposed scores, weightings, and comments. Forrester does not endorse any vendor, product, or service depicted in the Forrester Wave. Information is based on best available resources. Opinions reflect judgment at the time and are subject to change.”


SAP Brings BI and Advanced Analytics Together

SAPLumira Cloud

Explorer MobileExplorer

Web

SAPLumira

Search

Explore

Share

Acquire

Transform

Visualize

Design

Model

Build

Govern

Enterprise Information

Assets

Local

View

SAPPredictive AnalysisKXEN


SAP Predictive Analysis …

... Complete data discovery, visualization, and predictive analytics solution

... Integrated with SAP Lumira for data acquisition, data manipulation and visualization capabilities

… is one integrated solution for advanced data analysis and interactive data visualizations

… identifies trends, insights and discovers hidden patterns in the data

SAP Predictive AnalysisData Discovery Rich Visualizations Predictive Analytics


SAP Predictive Analysis Self Service for Business Analysts and Data Scientists

Provide Business Analysts with sophisticated algorithms to take the next step in understanding their business and modeling outcomes

• Perform statistical analysis on your data to understand trends and detect outliers in your business

• Build models and apply to scenarios to forecast potential future outcomes

• Breadth of connectivity to access almost any data

• Optimized for SAP HANA to support huge data volumes and in-memory processing

OVERVIEW


SAP Predictive Analysis Visualize, discover, and share hidden insights

TODAY

Library of advanced visualizations within the modeling tool

Share insights via Predictive Modeling Mark-up Language (PMML) and with other BI clients


SAP Predictive Analysis – Algorithm sources

HANA Predictive Analysis Library (PAL)

algorithms

Analysis is done within HANA (no movement of data) and controlled by SAP PA

Open Source ‘R’ integration algorithms

Data is brought to SAP PA and analysis is performed in the client

SAP PA Native algorithms

Data is brought to SAP PA and analysis is performed in the client

HANA ‘R’ integration algorithms

Analysis is done in R server attached to HANA and controlled by SAP PA


PAL - Algorithms supported in HANA 1.0 SPS7

Y

X Z

Association Analysis

Apriori

Apriori Lite

Cluster Analysis

ABC Classification

DBSCAN

K-Means

Kohonen Self Organized Maps

Agglomerate Hierarchical Clustering

Affinity Propagation clustering

Classification Analysis

C4.5 Decision Tree Analysis

CHAID Decision Tree Analysis

K Nearest Neighbour

Multiple Linear Regression

Polynomial Regression

Exponential Regression

Bi-Variate Geometric Regression

Bi-Variate Logarithmic Regression

Logistic Regression

Naïve Bayes

Support Vector Machines

Time Series Analysis Single Exponential Smoothing

Double Exponential Smoothing

Triple Exponential Smoothing

Outlier Detection Inter-Quartile Range Test (Tukey’s Test)

Variance Test

Anomaly Detection

Link Prediction Common Neighbours; Jaccard’s Coefficient; Adamic/Adar;

Katzβ

Data Preparation Sampling

Binning

Scaling

Convert Categorical to Binary

Other Weighted Scores Table

Statistical Functions – Univariate and Mutlivariate


Customizing SAP Predictive AnalysisAdd your custom R script as a component

Predictive Analysis provides an interface for users to add a new R component using a wizard

• Type the R script or import it from a file

• Add parameters required for the script (including model saving option)

• Use the visualizations available in R as part of the script

New R component can be created both in HANA online and non-HANA online scenarios


What is R?

R is a software environment for statistical computing and graphics Open Source, programming language plus a run-time environment

Over 3,500 add-on packages; ability to write your own functions

Widely used for a variety of statistical methods: linear and non-linear models, statistical tests, time series analyses, classification and clustering, predictive, etc.

More algorithms and packages than SAS + SPSS + Statistica

Who is using it? Growing number of data analysts in industry, government, consulting, and academia

Cross-industry use: high-tech, retail, manufacturing, CPG, financial services , banking, telecom, etc.

Why are they using it? Free, comprehensive, and many learn it at college/university

Offers rich library of statistical and graphical packages


R Integration for SAP HANA Functionality Overview

The R integration for SAP HANA enables the use of the R open source environment in the context of the HANA in-memory database

Establishes a communication channel between HANA and R for fast data exchange

Embed R script within SQL script and submit entire query to the HANA database.

As the plan execution reaches R-node, a separate R runtime is invoked using Rserve and input tables of R node passed to R process using improved data transfer mechanism.

SAP PA

SAP HANA R Server

SQL R script

R script


Options to use SAP Predictive Analysis


SAP Predictive Analysis – High Level Architecture

HTML5 Client

Predictive Analysis Backend

Prepare Room

Lumira Backend

Visualize Room

Share Room

Compose Room

Predict Room

Designer

PA specific Visualization

In Database Engine

Offline Engine

PA Online components

PA offline components

Offline R Engine

HANASybase IQ

PAL Scripts

R ScriptsR Server

HTTP

JDBC


Predictive Analytics in SAP Applications

Customer Engagement Intelligence

Shopper Insight

Sentiment Intelligence

Situational Awareness

Net Margin Analysis

Condition Based Maintenance

Demand Signal Management

Fraud Management


Empower the Modern Marketer with SAP Customer Engagement Intelligence

SAP Solution Elements Business Benefits

Better understanding of customer behavior by leveraging Big Data as an asset

Improve ROI of campaigns by targeting the right audience

Champion the delivery of a personalized consumer experience across channels

Real-time reporting on campaign success

Marketing optimization to drive revenue and margin

SAP Predictive Analysis to optimize marketing campaigns

SAP Audience Discovery & Targeting turning analytical insight into action for campaign management powered by SAP HANA


SAP Predictive Analysis 1.17Feature Description

Integration with SAP Lumira

• Data acquisition from a variety of data sources – SAP HANA (offline and online), universes, RDBMS, SAP ERP, XLS, CSV

• Data preparation, manipulation and visualization

Simplified UI for predictive analysis

• Operates in HANA and non-HANA scenarios

• HANA 1.0 SPS7 PAL support

• HANA R support – 5 algorithms out of the box

• Offline R support – 13 algorithms out of the box

Predictive specific visualizations

• Automatically created predictive algorithm specific visualizations

– Time series chart, regression chart, cluster viewer, decision tree and tag cloud

Customization support

• Custom R script support in HANA and non-HANA

– Ability to run any R algorithms

– Build your own visualization using D3 charts.

Enhance model consumption

• Advance Modeling, Partition data and choose the best model.

• Export PAL models and Analysis as SQL procedure

• Export and import models as PMML and proprietary format (SPAR)

Saving & Sharing predictive data and visualizations

• Save data in lums and automatic recreation of predictive specific algorithms visualizations on opening of .lums

• Sharing of predictive specific datasets and visualizations Story board.

Multi language support

• English, French, German, Japanese, Simplified Chinese, Russian, Portuguese, Spanish


TODAY Planned Innovations Future DirectionTODAY Planned Innovations Future Direction

SAP Predictive AnalysisProduct road map overview - key themes and capabilities

Integration with SAP Lumira

Data acquisition and manipulation

Save user created visualization

Share predictive data

Simplified HTML5 UI for predictive analysis

HANA and non-HANA scenarios

HANA PAL & HANA R support

Open source R support

Predictive specific visualizations

Customization support

Custom R script support

Enhance model consumption

PAL models , Analysis as SQL procedure

Export & import models as PMML/SPAR

Train Test Validate,

Infinite Insight Integration

Classification and Regression Analysis

Clustering Analysis

Saving predictive data & visualizations

Core Predictive/ Advance Analysis Feature Enhancement.

Model Comparison

Schedule Analysis

Algorithm Improvement, HANA PAL, Offline Algorithm

Model Visualization Improvements

SAP Infinite Insight Integration

Infinite Insight Modular Algorithm.

Generation of Model in SAP HANA SQL

Predictive Consumption / Scoring

Exporting HANA R Model as Stored Procedure.

Consuming PA Models in Cloud/Server

Core Predictive/ Advance Analysis Feature Enhancement.

Auto Modeling

Advance Modeling,

Large Data volume Visualization

Algorithm Improvements

SAP Infinite Insight Integration

(SAP Predictive Analysis becomes single, go-forward solution)

Support IFL Algorithms on HANA.

Integration with Modeler, Explorer, Factory, SNA

Infinite Insight Recommendation Integration

Consumption of Infinite Insight Artifacts.

Predictive Consumption / Scoring

PA Models in Lumira stories Cloud / Server

Offline Model Export as DB SQL

Offline R Models as Stored procedure.

This is the current state of planning and may be changed by SAP at any time.(Release 1.0.17)


Skills shortage


SAP InfiniteInsight Overview

BuildModel

ScheduleRefresh

PrepareData

DeployModel

ADS Creation

Data Manip & Prep

Text Analytics

Link Analysis

Viral Marketing

Influencers

Regression

Classification

Segmentation

Forecasting

Products

Targeted Ads

Website Content

In-Database

(Optimized SQL for

Teradata, SAP Hana, etc.)

Inline

(C++, PMML, Java, SAS,

etc.)

Refresh ADS

Retrain Model

Apply Scores

Notify on Exception


Modeler

Build your models

Access

Easily Integrate

InfiniteInsightScorer

Deploy your scores

InfiniteInsightFactory

Improve your models

Explorer

Prepare your data

SAP InfiniteInsight


Improve Insight Extend Reach Boost ROIConnect

Access to both data and metadata (name of columns, storage, indices and more)

Connectivity to major database platforms

Access to major proprietary file formats (SAS, SPSS, Excel, etc.)

Easily integrate into your existing data investments


Reusable Reduces Human Error Self-ServicePrepare

Create 1000’s of derived attributes

Define metadata once

Select time-stamped population

Builds analytic dataset automatically

Analytical data sets with clicks not code


Easy to Use Time to Market More ModelsBuild

Fully automated modeling process

• Regression

• Classification

• Segmentation

• Time series forecasting

• Association rules

Identify key variables

Executive and operational reports

Predictive power in days not months


Put scores into action

One-click deployment of scores into production

In-database scoring (SQL)

Interface with business apps via scoring equations in

• Java

• PMML

• SAS

Non-Intrusive Time to Value RepeatableDeploy


Refresh analytic data sets and models automatically

Deploy scores to production

Alert on data and model deviations

No Programming Scale Manage By ExceptionImprove

Every model at peak performance


Improve Insight Extend Reach Boost ROI

Social

Use social variables for enhanced prediction

Identify communities amongst your customers

Find influencers to make your campaigns viral

Improve insight with social networks


Adaptive Big Data Plug & Play

Recommend

Addresses any type of business questions

Make Product recommendations, targeting digital content

Social recommendations (e.g. friends) and targeted ads.

Personalize the recommendations


Why SAP advanced analytics

Real-time in-memory predictive &next generation visualization & modeling

Empower the Business

Extend the BI competency to advanced analytics

Embed predictive into LoB and industry solutions

Lend expertise

Bridge skills gap

In-memory processing

No data latencies

Big Data ready

In-time Actionable Insights

Reduced TCO

Streamline data management, data prep, model building and model scoring on database

Within the context of your Industry & LOB scenario

© 2014 SAP AG or an SAP affiliate company. All rights reserved.

Thank you!

Venkatesh Vaidyantahan, [email protected] Kumar KN, [email protected] Thukral, [email protected]

30-day Trial of SAP PA here: http://www.saphana.com/docs/DOC-3527

Community, Demo & Use cases: http://www.saphana.com/community/learn/solutions/predictive-analysis

SCN: http://scn.sap.com/community/predictive-analysis



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Real-time business with the SAP HANA platform for Big Data

Anil Kumar Damara

Director - Cognilytics

Agenda

Why Big Data

Big Data Potential

The power of Hadoop integrated with SAP HANA

Smart Data Access

SLT Replication

SAP Business Suite

Example for Real-Time Value powered by SAP HANA

Why Big Data

12 TB of Tweets in a Day

80%Of world’s datais unstructured

30 billion pieces of content shared on Facebook every

month

Expected Data in 2020 would be 35 ZB

5 Million Trade events per second

The Human mind processes about one PB in a sec , So 50

PB can store everything in min

4.7 billion searches on

Google per day

5 Billion people tweet,text,call and browse on mobile

phones daily

Walmart handles 1 Million transaction per hour

Big Data Characteristics

Build Vs Buy

HUMAN DRIVEN

EMAIL

WEB LOGS

DOCUMENTS

SOCIAL

• Data Landscape evolution

MACHINE DRIVEN

SATELLITE IMAGES

BIO-INFORMATICS

M2M LOG FILES

SENSORS

VIDEO

AUDIO

BUSINESS DRIVEN

OLTP

I.T. MUST MANAGE, GOVERN AND ANALYZE MORE DATA WITH MORE COMPLEX RELATIONSHIPS … IN REAL TIME … AT SCALE

ALL DATA TYPES

1X 10X 100X

BIG DATA TODAY

BIG DATA TOMORROW

SAP HANA – In-Memory Computing

Operational

Warehouses

Marts

Dimensional

Semantic

Information

Oracle DB2 SQL Other

BW TeraData Netezza

Mart Mart Mart

OLAP OLAP

IQ

Universe

?Queries Ad-HocDashboard

ETL

DATA QUALITY

Applications

Reports

OLAP

Mart Mart Mart

OLAP

Mart

HANA

Oracle/DB2/SQL/Other

BW/Netezza/Teradata/IQ

HANA

- 7 - SIT

te

mp

late

.pp

txClient logo

Client logo

How it is done

Any Device

Any AppsAny App ServerAny Apps

Any App ServerSAP Business Suite

and BW ABAP App Server

SAP Business Suite and BW ABAP App Server

JSONROpen

ConnectivityMDXSQL

Other AppsLocationsReal-timeHADOOPMachineUnstructuredTransaction

SAP HANA Platform

SQL, SQLScript, JavaScriptSQL, SQLScript, JavaScript

Integration ServicesIntegration Services

SpatialSpatial

Business Function Library

Business Function Library

SearchSearch Text MiningText Mining

Predictive Analysis Library

Predictive Analysis Library

DatabaseServicesDatabaseServices

Stored Procedure & Data Models

Stored Procedure & Data Models

Planning EnginePlanning Engine Rules EngineRules Engine

Application & UI Services

Application & UI Services

More than just a database

Next generation - SAP Real-time Data Platform

SAP Analytics

SAP Business

Suite

SAP Big Data Applications3rd Party

BI Clients

SAP Mobile

On Premise / Cloud

Custom Apps

Open Developer API’s and Protocols

Com

mon Landsc

ape

Managem

ent

SAP Enterprise Information Management

SAP Sybase Replication Server

SAP Data Services

SAP HANA Platform

SAP MDG and MDM

SAP Real-time Data Platform

SAP Sybase IQ SAP Sybase ASE

SAP Sybase SQLA

SAP Sybase ESP

Com

mon M

odelin

gS

ybase P

ow

erD

esi

gner

MP

P

Sca

le-O

ut

SAP NW BW

The power of Hadoop integrated with SAP HANA

With SAP in-memory computing platform, SAP HANA, you’ll have the ability to run big

data analytics on 80 terabytes of data, integrate with Hadoop, search text content,

harness the power of real-time predictive analytics, and more.

Exploit unstructured data such as text, documents, Web, and social media content

Deliver predictive insight with in-database data mining

Leverage open source R analytic processing

SAP HANA integration with Hadoop: enabling customers to move data between Hive

and Hadoop's Distributed File System and SAP HANA or SAP Sybase IQ server, which

will work to provide products that make use of HANA and Hadoop.

With SAP in-memory computing platform, SAP HANA, you’ll have the ability to run big

data analytics on 80 terabytes of data, integrate with Hadoop, search text content,

harness the power of real-time predictive analytics, and more.

Exploit unstructured data such as text, documents, Web, and social media content

Deliver predictive insight with in-database data mining

Leverage open source R analytic processing

SAP HANA integration with Hadoop: enabling customers to move data between Hive

and Hadoop's Distributed File System and SAP HANA or SAP Sybase IQ server, which

will work to provide products that make use of HANA and Hadoop.

The power of Hadoop integrated with SAP HANA …

As we understood so far that Hadoop can store very huge amount of data. It is well

suited for storing unstructured data, is good for manipulating very large files and is

tolerant to hardware and software failures.

But the main challenge with Hadoop is getting information out of this huge data in real

time.

As we understood so far that Hadoop can store very huge amount of data. It is well

suited for storing unstructured data, is good for manipulating very large files and is

tolerant to hardware and software failures.

But the main challenge with Hadoop is getting information out of this huge data in real

time.

1. Use SAP Data Services to extract:

Core entities (who, what, when, where, etc.)

Domains (voice of customer, public sector, enterprise, etc.)

Sentiment analysis (strong positive, weak positive, neutral, weak negative, strong negative)

2. Perform transformations

Map text into pre-defined structures

Cleanse, match, de-duplicate data

3. Load results quickly into EDW

Map text to structure

The power of Hadoop integrated with SAP HANA …Processing Text to extract relevant data from Hadoop


SAP Data Services: Simple GUI build and run ETL processSAP Data Services: Simple GUI build and run ETL process

Processing Text to extract relevant data from Hadoop


Parameter SAP HANA HADOOP

Data Storage Hot Data (high-value, often used data, in-memory)

Cold Data (- persist information for archival and retrieval in new ways, - don't want to structure in advance: Weblogs)

Maturity Incredible maturity of HANA's SQL and OLAP engines

Need to improve

Aggregation Speed Fast. Different ways of aggregationavailable

7x times faster

Simplicity of operation and storing data

Batch Jobs processing Very Efficient but high cost Very Efficient and Cost effective

Parallel Processing Very good parallelization on large system and near-linear scalability

Not Available

Software Stack SAP Proprietary Open Source


Explore Product Performance in Real-timeExplore Product Performance in Real-time


Sessionization

Big Data TypesBig Data Types

Reviews and Social MediaReviews and Social Media


BO Explorer for Big Data Exploration

Smart Data Access

Smart Data Access in SAP HANA which is a Virtualization Technique.

Smart Data Access is a technology which enables remote data access as if they are local tables in HANA without copying data into SAP HANA.

Data required from other sources will remain in virtual tables. Virtual tables will point to remote tables in different data sources.

It will enable real time access to data regardless of its location and at same time, it will not effect SAP HANA database. Customers can then write SQL queries in SAP HANA, which could operate on virtual table.

The HANA query processor optimizes these queries, and executes the relevant part of the query in the target database, returns the results of the query to HANA, and completes the operation.

Smart Data Access in SAP HANA which is a Virtualization Technique.

Smart Data Access is a technology which enables remote data access as if they are local tables in HANA without copying data into SAP HANA.

Data required from other sources will remain in virtual tables. Virtual tables will point to remote tables in different data sources.

It will enable real time access to data regardless of its location and at same time, it will not effect SAP HANA database. Customers can then write SQL queries in SAP HANA, which could operate on virtual table.

The HANA query processor optimizes these queries, and executes the relevant part of the query in the target database, returns the results of the query to HANA, and completes the operation.

2 Parts:

1) Initiating the Replication

2) Monitoring the Load and Replication

2 Parts:

1) Initiating the Replication

2) Monitoring the Load and Replication

SLT Replication – Real-Time

How to Replicate Data from SAP System to HANA using SLT How to Replicate Data from SAP System to HANA using SLT

Source SLT HANA

SAP Business Suite

Powered by SAP HANA

SAP ERP SAP CRM SAP SCM SAP SRM

SAP HANA PLATFORM

Smarter Business

InnovationsUnlock new growth opportunities

before your competitors do

Faster Business

ProcessesDrive your business at the speed

of market

Smarter Business

InteractionsEmpower people to decide and

act in the business moment

SAP HANA Text Analysis using Twitter

Twitter API

Tweets into SAP HANA

system

Run Text Analysis in SAP

HANA

Twitter API

Tweets into SAP HANA

system

Run Text Analysis in SAP

HANA

SAP BW powered with HANA

Technologies Used

SAP BusinessObjects Analysis, edition for OLAP

SAP BusinessObjects Design Studio

SAP BusinessObjects BI

SAP NetWeaver BW

SAP HANA

In case of data replication:

SAP LT

Technologies Used

SAP BusinessObjects Analysis, edition for OLAP

SAP BusinessObjects Design Studio

SAP BusinessObjects BI

SAP NetWeaver BW

SAP HANA

In case of data replication:

SAP LT

SAP Real Time Data Platform

BW CompositeProvider

BW InfoProvider

Real-time replication from SAP or 3rd party systems

inserts / updates from an HANA application

One-click conversion

Data to Decisions

o How the signals are being analyzed in real-time using SAP HANA In-memory computing

o In Marketplaces, observe what is going right and what is going wrong e.g: Consumer Selling

o How the signals are being analyzed in real-time using SAP HANA In-memory computing

o In Marketplaces, observe what is going right and what is going wrong e.g: Consumer Selling

Real-Time Value from Marketplaces

SAP HANA + SLT = Real-time Intelligence on streaming and operational DataRetail Industry – predictive buyer & seller behavior analysis

Business Challenges

Increase conversion rates from free buyer and seller

Increase the average revenue per buyer player

Decrease churn – keep paying players playing longer

Technical Challenges

Leverage real-time data processing in SAP HANA and classification algorithms with R integration for SAP HANA to deliver personalized context-relevant offers to players

Analyze vast amounts of historical and transactional data to forecast buyer and seller behavior patterns

Benefits

Real-time insights

Per seller profitability analysis and increased understanding of seller and buyer behavior

Increase data volume and processing capabilities to communicate personalized messages to players

At one of is our strategic retail client, we have successfully implemented signal detection system leveraging SAP HANA as the in-memory to transform its marketplace, optimizing the buyer and seller experience. And simplified operations and transformed business.

Senior Managing Director – Cognilytics

“ ”

5,000 Signals per second loaded onto SAP HANA (not possible before)

10%–30%increase in revenue per year

Interactivedata analysis leading to improved design thinking and marketplace planning

References

In-memory Computing with HANA

http://www.sap.com/pc/tech/in-memory-computing-hana/software/platform/database.html

BIG DATA Platform Capabilities & Benefits

http://scn.sap.com/community/hana-in-memory/blog/2013/11/12/big-data-platform-capabilities-benefits

A Big Data Platform for Real-Time Business: How Customers Use SAP HANA

http://events.sap.com/sapphirenow/en/session/2289

SAP HANA Tutorial

http://saphanatutorial.com/sap-hana-and-hadoop/

In-memory Computing with HANA

http://www.sap.com/pc/tech/in-memory-computing-hana/software/platform/database.html

BIG DATA Platform Capabilities & Benefits

http://scn.sap.com/community/hana-in-memory/blog/2013/11/12/big-data-platform-capabilities-benefits

A Big Data Platform for Real-Time Business: How Customers Use SAP HANA

http://events.sap.com/sapphirenow/en/session/2289

SAP HANA Tutorial

http://saphanatutorial.com/sap-hana-and-hadoop/

Summary & Key Takeaways

Real-time computing is the key.

Immediate and direct access to the latest data in real time.

Unstructured data - Hadoop.

OLTP and OLAP combining into one System – SAP HANA

Smart Data Access and SLT replication.

Real-time Actionable Insights

25

Questions

Please complete this session evaluation

Thanks for attending

HANA Modeler FeaturesWhat’s New?

Raghavendra Rao

SAP Labs

© 2014 SAP AG or an SAP affiliate company. All rights reserved. 2Public

SAP HANA SPS 08 – Feature Overview

Modeling Enhancements

• Enhanced SAP HANA Modeling capabilities

Variable/Input Parameter mapping to external views for value help

Data types specifications - allow decimal/float type without specifying length and scale

Currency conversion - support to specify the target data type also for base measures / configurable currency columns

Sorting support within of parent/child hierarchies

Set default schema mapping at package level

Mass-copy to allow sub-packages to be selected

Show productive system alert for edit/update/delete activities

Introducing performance analysis capabilities (partitioned tables, number of rows)

Support for Unicode characters in view/column/… names

UI and usability enhancements (new tree map control in union view, join icons, …)

BW connections enhancements


SAP HANA SPS 08 - Modeling Enhancements

Value Help Views – Variable and Input Parameter Mapping Support

Parameter passing to external views for value help

Variables and Input Parameter can be mapped to variables and input parameters from external views

– Allows filtering and customizing value help lists from external views

– Supported with Analytic- and Calculation Views (Graphical and Script)

Manage Mapping dialog

– Directly enabled in variable/input parameter creation dialog

– Supports mapping source differentiation via “Select Type”



Numeric data type handling enhancements

Relaxed FLOAT and DECIMAL data type specifications

DECIMAL data types can be specified without mandating length and scale

– Internally treated as floating-point decimal with varying length and scale,length is derived from values at runtime

FLOAT data type can be specified without mandating length

– Internally treated as 64-bit double data type

Relaxed data type specification supported with

– calculated columns dialog,

– input parameters for direct and static type, variables

– union-node constants

– procedures and script-based calc views

Can be left unspecified



Naming conventions enhancements

Support for Unicode characters in names

Unicode characters can now be used in view names, column names, input parameters, variables, hierarchies, calculation view node-names, etc. …

“Field Name Preferences” allows control the use of unicode characters

List of restricted characters (which are not allowed)

– \ / : * ? “ < > |. ; ‘ $ % , ! # + and space

– For restricted measures and hierarchies use of slashes(/) as beginning characters is allowed



Currency conversion enhancements

Data Type for measures with currency conversions

Data Type and precision for the conversion values can be specified independent from the input data

– The inherited data type and precision may have too generic precision definition resulting in rounding errors after the conversion.

– This allows to specify sufficient precision during conversion

– Only numerical data types with decimal places are allowed

– Supported for base measures, not with calculated measures



Hierarchy enhancements

Ordering in Parent/Child Hierarchies

Order By columns can be specified

– Previously parent-child hierarchies were ordered according to the leaf nodes in the child column and from that the natural ordering of their ancestors follows accordingly.

– Now, on the advanced tab of the parent child hierarchy dialog we allow specifying sort attributes with the sort direction.



Introducing performance analysis capabilities

Performance Analysis Mode in Modeling Environment

Introduction of performance analysis hints and indicators inside the HANA Model Editor

– Manually switched on or defaulted switched on

– Hints and indicators about table partitioning and number of rows (threshold as preference)

Scenario indicators for partitioned tables (icon)and exceeded row thresholds

Switching on performance analysis mode

View details pane: indication about partitioning type by icon (hash, range, …)

Performance analysis: more partitioning and row count information.



Calculation View Union Modeling Enhancements

Union Modeling Enhancements

New graphical tree-map control for unions

– Higher performance and usability when handling large structures with the union-node

– Scrolling, selecting, removing etc. are much faster

Query behavior for constant mapping columns

– A “Empty Union Behavior” flag allows to determine if queries on constant value columns shall return values, e.g. for value help queries in applications



Managing Model Content Enhancements

Mass Copy Enhancements

Allows copying of content into sub-packages / multiple packages as target package

Package Mapping configurations can be stored independent from system tables "_SYS_BI"."M_CONTENT_MAPPING"

– Can now be stored in developer’s local eclipse workspace

– Warning issued if switched back to system tables



Managing Schema Mapping Enhancements

Define specific default schemas

Schema content may derive from multiple / different back-end or authoring environments

In order to ease managing of schema mapping in such scenarios, package-specific schema mapping (which overrides the default schema mapping) can be maintained in _SYS_BI.M_Package_Default_Schema

– Has to be maintained manually (SQL)

– mapping with package specific default schema

Additionally, the „schema“-property of a catalog table in the modeleditors is editable (combo-box dialog)

Default Schema Mapping

Overruled by Package-specificSchema Mapping



Model Editor User Interface Enhancements

Miscellaneous user interface enhancements

Color schema harmonization and shading selected node

Column icon use across model

Show complete editor palette

Matching join and union icons across all editors

Filter Tooltip in Details panel

– show filter expression on hover over in details pane (same as scenario panel)



Production Alert and BW connection enhancements

Miscellaneous alert and configuration enhancements

Production System Alert

– Editing, deletion, object activation, … actions issued on systems as Production Systems (HANA system configuration) will issue a visual alter (indicator) or extra pop-up

BW connection configuration enhancements

– „SAProuter String“ for BW-Models input connection

– Java connection encryption betweenHANA Studio and BW back-end(requires client crpyto libs installed)



Modeling Productivity – Error Handling/HANA AnswersIntegration

Extended Error Handling with SAP HANA Answers (answers.saphana.com)

In extension to documentation and help, SAP HANA Answers.com will be introduced to SAP HANA Studio as crawl source of information

E.g. adds information from SCN and others

Displays embedded in HANA Studio or outside

Integrated with HANA Studio views (job log, …),editors, wizards. Called via key from selected textor feature.

Independent feature to install

Questions


Disclaimer

This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation is not subject to your license agreement or any other agreement with SAP.

SAP has no obligation to pursue any course of business outlined in this presentation or to develop or release any functionality mentioned in this presentation. This presentation and SAP’s strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without notice.

This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. SAP assumes no responsibility for errors or omissions in this document, except if such damages were caused by SAP intentionally or grossly negligent.


How to find SAP HANA documentation on this topic?

SAP HANA Platform documentation

What’s New – Release Notes

Modeling– SAP HANA Modeling Guide

Development– SAP HANA Developer Guide

References – SAP HANA SQL Reference

• In addition to this learning material, you find SAP HANA documentation on SAP Help Portal knowledge center athttp://help.sap.com/hana_platform.

• The knowledge center is structured according to the product lifecycle: installation > security > administration > modeling > development.

So you can find e.g. the SAP HANA Modeling Guide in the modeling section and so forth …


Thank you

Contact information

B Raghavendra RaoAssociate [email protected]



No part of this publication may be reproduced or transmitted in any form or for any purpose without the express permission of SAP AG or an SAP affiliate company.

SAP and other SAP products and services mentioned herein as well as their respective logos are trademarks or registered trademarks of SAP AG (or an SAP affiliate company) in Germany and other countries. Please see http://global12.sap.com/corporate-en/legal/copyright/index.epx for additional trademark information and notices.

Some software products marketed by SAP AG and its distributors contain proprietary software components of other software vendors.

National product specifications may vary.

These materials are provided by SAP AG or an SAP affiliate company for informational purposes only, without representation or warranty of any kind, and SAP AG or its affiliated companies shall not be liable for errors or omissions with respect to the materials. The only warranties for SAP AG or SAP affiliate company products and services are those that are set forth in the express warranty statements accompanying such products and services, if any. Nothing herein should be construed as constituting an additional warranty.

In particular, SAP AG or its affiliated companies have no obligation to pursue any course of business outlined in this document or any related presentation, or to develop or release any functionality mentioned therein. This document, or any related presentation, and SAP AG’s or its affiliated companies’ strategy and possible future developments, products, and/or platform directions and functionality are all subject to change and may be changed by SAP AG or its affiliated companies at any time for any reason without notice. The information in this document is not a commitment, promise, or legal obligation to deliver any material, code, or functionality. All forward-looking statements are subject to various risks and uncertainties that could cause actual results to differ materially from expectations. Readers are cautioned not to place undue reliance on these forward-looking statements, which speak only as of their dates, and they should not be relied upon in making purchasing decisions.

© 2014 SAP AG or an SAP affiliate company. All rights reserved. 2

Summary

• Enables employees to quickly find and contact the best expert and talent inside the organization.

• Searches skills and talent data wherever it is – integrating with any system – even with LinkedIn

• Extends the SuccessFactors BizX Suite


Design

• Built on Hana Cloud Platform.

• Flexible Framework supporting multiple Skill DB sources (Interface -Based)

• Ranked result list based on level of expertise, Proximity with Employee


Architecture

HANA Cloud Platform

Initial Load

Periodic Delta

Updates

HANA DB

Search Module

SAP UI5 Front end

LinkedIn

Wikipedia / GeoName

Org Data

Skill Data

Interfaces

1. Load Initial Data to get Org and Skill

master data

2. Run WhoCanHelpMe

App

3. Allow Access to LinkedIn

Profile

4. Put Search criteria and

ENTER

Skill Data

Other Sources 1

Skill Data

Other Sources nLinkedIn Id is

linked with SAP ID

Org Data File in Fixed Format

Manual Initial Load

Implementation


Tight Coupling as Extension with SuccessFactors

Data

• Use of employee’s organizational data

• Use of employee’s skill and talent data

User interface

• Single Sign on from SuccessFactors Home page

• Quick access of the extension from the homepage

• And vice versa direct access from the extension to SF Employee Central organizational data


SFAPI

• Compoundemployee

• background_specialassign

• background_certificates

• background_courses

• background_funcexperience

• background_industryexperience

• background_languages

• background_leadexperience

• background_projectexperience

• background_techskills

SuccessFactor API’s

ODATA

• SelfReportSkillMapping

• RatedSkillMapping


Home page


Performance Analysis

• Powered by two alternative search engines:

• HANA Specific

• Non-HANA

• Two options to prioritize search

• AND: where the search engine finds profiles matching all the supplied search terms

• OR: where the search engine finds profiles matching any of the supplied search terms


Analysis AND vs OR

0

1000

2000

3000

4000

5000

6000

7000

8000

932 30832 329832 628832 1017532

Res

po

ns

e t

ime

in

mse

c

Total number of records in DB

OR Search Engine

JPA DB

HANA DB

JPA Total Time

HANA Total Time

0500

100015002000250030003500400045005000

932 30832 329832 628832 1017532

Res

po

ns

e t

ime

in

mse

c


AND Search Engine

JPA DB Time

HANA DB Time

JPA Total Time

HANA Total Time


Analysis HANA vs Non-HANA(JPA)

0

500

1000

1500

2000

2500

3000

3500

74 32 141 57469

169593

217

1176

266

1213

373

1021

422

1513

1156

15101591

2203

234328

7

28

7

28

7

287

28

7

Res

po

ns

e t

ime

in

mse

c


HANA code pushdown

Selected Records

HANA Total Time

HANA DB Time

0

2000

4000

6000

8000

10000

12000

14000

826 471 412 482 1658146523772561

585044322086

722 1401 693

26421588

34222615

7540

4174

28

728

7

287

287

28

7

res

po

ns

e t

ime

in

mse

c


JPA Query

Selected Records

JPA Total Time

JPA DB Time


http://marketplace.saphana.comcom

Try it out today!Find more information on


Summary

• One Stop Skill Searching Point

• Combined with Professional Network Search

• Filling the gap for SuccessFactors BixZ Suite

• Build on HANA Cloud Platform

• Performance optimized with code push down to SAP HANA DB

• Interface Based Architecture


Key Takeaway

• SIMPLIFY experts search

• Intuitive USER EXPERIENCE using SAPUI5

• EXTENDING SAP Cloud Product Portfolio


Thank you

Shibaji Chandra , Vijay Singh RajputSAP Global DeliveryGurgaon

SAP HANA Cloud PortalVikrant Raj

Deloitte

Agenda

Platform as a Service(PaaS)

SAP HANA Cloud Portal

Product portfolio for SAP NetWeaver Portal

Administration & Authoring

Development Process

On-Premise Integration

Future Steps

Platform as a Service(PaaS)

PaaS delivers cloud-based application development tools, in addition to

services for testing, deploying, collaborating on, hosting, and

maintaining applications.

It enables customers and partners to rapidly build, deploy, and manage cloud-

based enterprise applications that complement and extend your SAP or non-

SAP solutions, either on-premise or on-demand.

SAP HANA Cloud Platform,

Platform-as-a-Service

offering from SAP, is an in-

memory cloud platform

based on open standards.

Product portfolio for SAP NetWeaver Portal

SAP HANA Cloud portal

Cloud-based solution for easy

creation and management of attractive

business sites designed for mobile

consumption out of the box.

A true portal Platform-as-a-Service

product.

SAP NetWeaver 7.3 Portal Proven, secure, mobile-ready enterprise portal platform enabling users to centrally access enterprise assets Enterprise Workspaces 1.1 SAP add-on solution empowering end users with focus on usability and mobile consumption to increase productivity for both individuals and teams SAP Portal content / site management by OpenTextSAP add-on solutions for enhanced content, document and web site management optimized for SAP NetWeaver Portal

SAP HANA Cloud Portal

Portal Platform as a Service (pPaas)

based lean portal, mashing and extending

on premise and cloud scenarios

• Enable lines of business to quickly and easily

create attractive and business-driven sites

• Arm IT departments with an easy-to-

administer, lean portal platform to

efficiently extend on-premises and cloud

scenarios with minimal investments

Key Elements

• Lean Portal – Quickly up and running!

• Create your own business site in minutes.

• Simple drag & drop interface

• Hosted by SAP public cloud

• Runs on top of SAP HANA Cloud

• AppStore-like experience

• Mobile first midset - designed for mobile consumption using HTML5 for

dynamic adaption to a range of devices

• Embraces industry technology standards (OpenSpcial, SAML2, CMIS)

• Enables secure and reliable integration with on-premise for leveraging

existing on-premise assets

• Fast branding and customization for multiple brands

High Level Architecture

Administration & Authoring – Creating Sites

Task Description

Create a new site Create a new site in the Site Directory. Open it for editing in the Authoring Space.

Create a page hierarchy

Define the structure of your site by adding pages and subpages.

Define page settings Define page name, page access level, and page navigation alias.

Assign a site theme Select a theme from the list of available themes, and apply it to your site.

Administration & Authoring – Working With Content

The main building blocks in SAP HANA Cloud Portal are widgets

developed using the OpenSocial standard.

• Document widget• HTML Viewer widget• Image widget• List Builder widget• Horizontal widget• Logon widget• Rich Text Editor widget• Navigation Menu• SAP Jam Feed widget• Social Networks widget• URL widget• Video Player widget

Administration & Authoring - Site Access and Permissions

Technical roles, used to manage site authoring permissions

Organization roles, used to manage access to published sites

Site Guest role, used to grant special access to individuals outside the

organization

Development Process

Developer Site Administrator Site Author

• Designs Solution

• Writes widget code

• Uses site CSS in widgets

• Deploys on SAP HANA Cloud Platform

• Adds widgets to Content Catalog(auto-discovery)

• Defines/modifies site themes

• Builds sites(create pages, places widgetson pages, defines navigation\hidden pages)

• Assign site theme• Customize widget

properties

On-Premise Integration

• Create a Gateway service using the Service Builder.

• Install and configure the SAP HANA Cloud connector.

• Define the SAP HANA Cloud destination.

• Deploy the destination to the Cloud Portal landscape.

Future Steps

• User experience and social

Expand site consumption, customization and social

enablement via rich toolsets and across any device

• Content management systems and integration

Enhanced integration capabilities across heterogeneous

environments via industry standards and protocols

• Platform

Leverage an enterprise scale cloud portal platform to

securely run your operation

• Development

Consume standards-based services to build, model and

configure portal environments from individual widgets to

complete portal sites

References

http://scn.sap.com/

https://help.hana.ondemand.com/

http://www.saphana.com/

https://store.sap.com/

Questions

July , 2014

Transforming the BW landscape to HANA EDW in NW BW 7.40 SP07

This presentation outlines our general product direction and should not be relied on in making a purchase decision. This presentation is not subject to your license agreement or any other agreement with SAP. SAP has no obligation to pursue any course of business

outlined in this presentation or to develop or release any functionality mentioned in this presentation. This presentation and SAP's strategy and possible future developments are subject to change and may be changed by SAP at any time for any reason without

notice. This document is provided without a warranty of any kind, either express or implied, including but not limited to, the implied warranties of merchantability, fitness for a particular purpose, or non-infringement. SAP assumes no responsibility for errors or

omissions in this document, except if such damages were caused by SAP intentionally or grossly negligent.

© 2014 SAP AG or an SAP affiliate company. All rights reserved. 2 Internal

This presentation outlines our general product direction and should not be relied on in making a

purchase decision. This presentation is not subject to your license agreement or any other

agreement with SAP. SAP has no obligation to pursue any course of business outlined in this

presentation or to develop or release any functionality mentioned in this presentation. This

presentation and SAP's strategy and possible future developments are subject to change and may

be changed by SAP at any time for any reason without notice. This document is provided without a

warranty of any kind, either express or implied, including but not limited to, the implied warranties of

merchantability, fitness for a particular purpose, or non-infringement. SAP assumes no responsibility

for errors or omissions in this document, except if such damages were caused by SAP intentionally

or grossly negligent.

Disclaimer

Agenda

Overview of SAP’s EDW Strategy

EDW with BW on HANA(BW + HANA)

BW + HANA + IQ

Success Stories

Agenda


EDW with BW on HANA( BW + HANA)

BW + HANA + IQ

Success Stories


Now that Suite works on Hana, do I even need BW?“


Unlock The Power of Your Data Across The Enterprise Enterprise Data Warehousing – the single point of truth

Enterprise Data Warehousing - why

– Consolidate the data across the enterprise to get a consistent

and agreed view on your data

"Having data is a waste of time when you can't agree on an interpretation."

– Combine SAP and other sources together

– Standardized data models on corporate information

– Supporting decision making on all organizational levels

EDWs require a Database plus an EDW application

EDW with SAP NetWeaver BW -

a flexible and scalable EDW application

– Highly integrated tools for modeling, monitoring and managing the EDW

– Open for SAP and non-SAP systems

– Agile data modeling using BW workspaces

– Runs on top of HANA and other RDBMS

– Easy consumption of HANA Data Mart scenarios via virtualized data access


Recent Challenges in EDW

Challenge Challenge

Category 2010 2011 2012

DB size (TB) A 3.2 4.8 5.5

DB growth per month

(GB) A 80 145 200

Average number of

users logged into the

system

A 150 200 400

Number of queries

per day A 4000 5000 8000+

Number of

Infoproviders (for

reporting)

B 395 1002 842

Number of DSOs B 263 292 320

A One is everything around

processing large amounts of

data, i.e. bulk loads, analytic

querying, table partitioning,

scalability, performance etc.

B Around the processes and the

data models inside the data

warehouse.


The Data Warehousing Quadrant data

volu

me

huge

modest

number of data models, sources, … modest huge

internet scale business process

(e.g. Ebay, Amazon, …) generating

huge amounts of (sensor) data

fairly modest challenges regarding

semantics, consolidation, harmoni-

zation, integration with other data

few data sources

mix of scenarios with small and

large amounts of data

many (1000s to 10000s) of data

models

many (100s to 1000s) different data

sources

data mart type of setup or

operational (OLTP) analytics

modest number of tables

modest (need for) integrations

between data models

VLDW XLDW

EDW Data Mart

more scenarios

more combinations of

scenarios

m

ore

gra

nu

lar

data

se

nso

r / b

ig d

ata

m

ore

sce

na

rios

HANA

BW


Benefits of Data warehouse

For Data consolidation

Mobility

Business Content

Planning – BPC Unified

Hot & cold data management- Big Data

Now that Suite works on Hana, do I even need BW?“

Yes ,to list out few reasons for the same


SAP’s strategic EDW solution SAP BW 7.4 on HANA

… simplify the data modeling processes

… increase the agility of the Enterprise Data Warehouse

… reduce the complexity of the EDW landscape

… combine the strengths of an SQL oriented approach with an Integrated EDW application

Only the combination of BW and HANA enables us to achieve the same

Seamless consumption of

data

Reuse BW services to manage and analyze

the data

One common modeling

environment

Process large amounts of data

faster

SAP EDW strategic Focus will be on HANA EDW (BW on HANA)

But Continue to Support RDBMS databases for existing and future releases

Agenda


EDW with BW on HANA(BW + HANA)

BW + HANA + IQ

Success Stories


SAP BW on HANA – Smarter, simpler, more efficient Min 7.30 Sp05 >

How Does BW running on RDBMS differ from BW running on HANA ? Recommended 7.40

HANA Stack

RDBMS

Traditional Stack

SAP NetWeaver BW

Data Modeling

Planning

Data Management

OLAP Pro

ce

ss

Orc

hes

trati

on

Data Schema

&

Data

SAP BW on HANA

Data Modeling

Planning

Data

Management

OLAP Pro

ce

ss

Orc

hes

trati

on

Push Down

HANA as the Primary Database for BW and

Foundation for new Applications

Enhanced Data Modeling

Common Eclipse based Modeling Tools

BW/HANA Smart Data Access providing the logical

EDW

Easy integration of external data models with

Open ODS Layer

Further reduce data layers in BW via Operational

Data Provisioning

Push down further processing logic to HANA

BW Analytic Manager

HANA Analysis Processes

BW Transformations

PAK – Pushing down more planning semantics

Enhanced mobile enablement

Converged planning solutions

BW Content optimized for HANA


Modelling in BW on HANA

HANA


Common modeling tools SAP BW 7.4, SP5 on HANA

Common user experience via a central, unified modeling

environment

Attractive, flexible and simplified BW modeling tools

Harmonization BW and HANA modeling environments

Integration of BW and HANA models in one modeling approach

Integrated development & modeling environment across

– SAP HANA Modeler,

– BW Modeling

o New developed native Eclipse based modeling tools for

Open ODS View and New CompositeProvider

– ABAP Development Tools

– …


Modeling Tools – Maintenance CompositeProvider SAP BW 7.4, SP5 and on HANA

Common user experience via a

central, unified modeling environment

New metadata object CompositeProvider

as abstraction object of the query to the

underlying technical persistence objects

Left:

– Scenario definition (Join, Union)

Right:

– Join condition

– Mapping of Source Objects to Target

Output structure


Modeling Tools – Open ODS View SAP BW 7.4, SP5 and on HANA

Common user experience via a

central, unified modeling

environment

New metadata object • OpenODSView to integrate external

data models into BW

Left • technical Information about Source

Fields

Right • General information

• Associations to OpenODSViews or

InfoObjects

• Characteristic-specific Properties such

as Authorization relevance and

Referential Integrity

• Reporting Properties such as Display

and Query Filter behavior


Field based modeling in BW on virtual HANA Tables SAP BW 7.4, SP5 on HANA

* Pilot only

(Note 1922533)

Open ODS View offers

• Metadata object as an abstraction layer for underlying source

object

• HANA virtual tables as supported source objects via SDA

• Querying on field level

• Supported for Teradata, Sybase ASE/IQ, Hadoop

• Optimized Query execution by pushing down to HANA

Easy assignment of semantics

• Underlying object (Table, DB View, DataSource) can be tagged

as Text, Master data or Facts

• Single fields of the object can be linked to already existing

Open ODS Views or InfoObjects

Use case 1

• Access existing HANA application

• Migrate existing RDBMS model to HANA

and consume via BW

Use case 2

• Replicate data from RDBMS (e.g. external tracking system) into

DSO (with fields) leveraging BW services for delta calculation

and request management

Virtual Access BW Managed Persistence * Virtual Access

BW Query

Virtual Table

BW Query

Open ODS Layer Open ODS Layer

DSO w/ fields*

Persistent

Open ODS View

Virtual

Open ODS View

Virtual

Virtual Table

Smart Data

Access

BW on

HANA

External Sources

Table/View Table/View


Smart Data Access SAP BW 7.4, SP5 on HANA

Enhanced Business Flexibility by

providing “the logical EDW”

Data Federation in diverse EDW landscapes

• Smart data access – read access to relational and

non-relational sources via ODBC

• Enables access to remote data access just like

“local” table

• Supports data location agnostic development

• No special syntax to access heterogeneous data

sources

• BW based Analytic Services on external data

Scenario

• Make other DWHs transparent to HANA

• Non-disruptive evolution from virtual table to

persistent structure by establishing ETL without

major effort

• Consolidating / rationalizing the DWH landscape

• Consumption of HANA datamart scenarios from

second HANA database

HANA Smart Data Access Layer

Query

BW Virtualization Layer

Composite Provider, Open ODS View

Teradata

Hadoop SAP HANA

ASE

IQ

Virtual Tables HANA Tables


Automatic generation of HANA models SAP BW 7.4, SP5 on HANA

BW Schema

generates

HANA Schema

HANA

View

InfoCube

DSO

Master

data

HANA

View HANA

View

Enhanced

HANA View

Enhanced Metadata interoperability between BW and HANA

HANA Model generation

Triggered from BW InfoProvider – push

– Complements BW model import from HANA Modeler

– Analysis Authorization: Automatic sync between HANA and BW

– Object changes include HANA model impact analysis

Direct consumption of BW data via generated HANA views

– SAP Lumira, BO Explorer, SQL

Scenario

Major footprint of scenario in BW

Usage of generated view in HANA Studio to build own data

models using BW data and HANA native algorithms


High Cardinality InfoObjects SAP BW 7.4, SP5 on HANA

Enable business scenarios which require extremely

high volume of Master Data e.g. sales invoice analysis

High Cardinality InfoObjects

InfoObjects can be flagged as “High Cardinality”

– No SIDs generated

o Thus overcoming the 2 billion records limitation

– Support Attributes, Texts, Compounding, Time dependency

Can be used in Data Store Objects

Enabled for analysis and planning


Operational Data Provisioning (ODP) Technology SAP BW 7.4, SP5 on HANA

SAP ERP Extractors Operational

Data

Provisioning

HANA Views

Source BW Embedded Analytics

Target BW

SAP DataServices

SLT

Provider Subscriber /

Consumer

ODQ

Unified technology for data provisioning

and consumption

Enables extract once deploy many architectures

for sources

Unified configuration and monitoring for all

provider and subscriber types

Time stamp based recovery mechanism for all

provider types with configurable data retention

periods

Highly efficient compression enables data

compression rates up to 90% in Operational Delta

Queue (ODQ)

Quality of service: „Exactly Once in Order“ for all

providers

Intelligent parallelization options for subscribers in

high volume scenarios

*

*

*

*) New with SAP BW 7.4


Simplified trigger based table replication to BW with SLT SAP BW 7.4, SP5 on HANA

New source system type ODP-SLT

• SLT Real-Time push in Operational Delta Queue (ODQ)

• Direct Update to BW InfoProviders

Scheduled or real – time daemon

Automatic change notification for daemon

• Set up of SLT replication from SAP BW

Benefits

• Simplified data flow

• PSA no longer required

• Flexible recovery options

• Consumption of ODQ by multiple subscribers

• Reduced data latency

InfoProvider

DTP

ERP Source System

Table

Operational Delta Queue

(ODQ)

SLT

Operational Delta Queue

(ODQ)

SLT

Scheduled

scenario

SAP BW

Table

Real-Time

DTP

Real-time scenario

InfoProvider

ODP DataSource B ODP DataSource B


Open Hub Service SAP BW 7.4, SP5 on HANA

Extending the reach of Open Hub Service to

provide HANA applications with BW query and

InfoProvider data

• Export data from BW directly to tables residing in any

RDBMS supported by SAP

• Supported for Sybase ASE and IQ as well

• Delta extraction for InfoProviders and DataSource

• Query snapshots via QueryProvider are possible

BW Schema

SAP BW Schema

SAP HANA Schema(s)

SAP HANA *) Available since BW7.3

powered by SAP HANA SP8

*)

InfoCube

Query

MasterData

DSO

Any DB supported

by SAP

SAP BW

Open Hub

Service


The OLAP Compiler for HANA Intro

BW / BEX Query Designer

is the design tool for the Analytic Manager

Analytic Manager

has a wide variety of OLAP functions.

converts the query definition into a ABAP runtime object (BW

Query)

Generates calculation scenarios for those BW Query

operations which can be performed in HANA directly

Pushing down BW Analytic Manager (OLAP)

operations down to HANA provides

Excellent query performance

Additional business insights by overcoming existing ABAP

based limits – deep granular data can now be analyzed (e.g.

counters on order items level) In Memory Database

Calculation and Planning Engine

Row & Column Storage

BW / BEX Query Designer

BW Application Server

Analytic Manager

BW Query

Calc.-views /

Calc. scenarios


HANA Analysis Process SAP BW 7.4, SP5 on HANA

Enhanced analysis capabilities

Execute HANA-native functions

directly on BW InfoProvider data e.g.:

– Clustering, association algorithms,

regression analysis, anomaly

detection, weighted score, exponential

smoothing, etc.

Execute complex and data intensive

processes on HANA without loosing

the integrity and integration with the

BW environment

Materialize the result of a HANA

Analysis Process in HANA for further

processing – automated

Supporting also a scheduled batch

processing use case

Source Function Target

BW InfoProvider AFL(PAL, …), Procedure,

L-Script, R-Script BW InfoProvider

BW Process Management


Planning LOB enablement

Combine the best of three worlds to

a unique planning solution

(HANA, BPC, BW-IP)

Combines the

• …successful EPM Excel add-in

• …flexible BPC admin-UI

• …powerful BW-IP / PAK planning manager

• …super-fast HANA planning engine

Selected features

• Full PAK-model compatibility

• Business process flows (BPF)

• Work-status

• Data auditing

• Easy upload scenario

• LOB authorizations

BPC NW ‘unified’

(10.1)

BPC NW

• user experience

• collaboration

• data flexibility

BW-IP

• EDW-integration

• Built-in functions

HANA

• Unprecedented speed


BW Query & ODATA Services SAP BW 7.4, SP5 on HANA

Enable home-grown, straight forward planning

applications and embedded planning, e.g. on

mobile devices

Easy to use

• Queries flagged as ‘OData’ in the BEXQuery

Designer offer an external planning and reporting

Service interface

Standard compliant

• Fully integrated into the ODATA specification

Robust and flexible

• Stateless cell-wise data input-protocol


New Business Content optimized for BW on HANA

• New analytics combining capabilities of SAP

HANA and SAP NetWeaver BW

• Provides additional analytic solutions for

existing BW on HANA customers

• Follows the LSA++ architecture

• Provides higher level of details (line items, …)

• Implements mixed scenarios HANA Content +

BW Content

• Provides optimized transformation for HANA

• Offers more flexibility in data acquisition and

reporting

• Makes use of the consolidated InfoObjects

• Find further information in the SAP Help – BI

Content documentation and see the extended

presentations on the HANA optimized

Business Content in SCN

http://help.sap.com/saphelp_nw74/helpdata/de/6a/f247bf0ce745eab5648a309fbd784e/frameset.htm







http://scn.sap.com/community/data-warehousing/business-content-and-extractors

Agenda



BW + HANA + IQ

Success Stories


HANA DB

InfoProvider

Near-line

Storage

Acquisition

NLS Interface

BW

Access - very frequently frequently not frequently rarely

• Optimized NLS load

performance using IQ Loader

functionality

• SAP HANA and IQ share the

same columnar paradigm

• NLS data compression around

90%

• Can handle large data volumes

• Suitable for ad-hoc queries with

long history

• Minimum administrative effort

• Helps to optimize the memory

usage of HANA

Strategy & Definition BW powered by SAP HANA and Sybase IQ NLS


Big-data analytics: issues Dealing with volume, variety, velocity, costs, and skills

Big

Data

Analytics

Managing and harnessing terabytes of data

Volume

Harmonizing silos of structured and unstructured data

Variety Lack of adequate skills for nonstandard platforms and application programming

interfaces (APIs)

Skills

Keeping up with unpredictable data and query flows

Velocity

Very expensive to acquire, operate, and expand

Costs


BW powered by HANA and Sybase IQ Near-Line Storage (NLS) Architecture - Overview

BI Clients

SAP

HANA Sybase

IQ

MultiProvider Transient

Provider InfoCube/DSO

Near-Line SDK

SAP Netweaver BW 7.3x

Partner

OEM

BW NLS4IQ

SAP Native

An SAP - owned BW NLS

implementation for Sybase IQ offers a

fully integrated solution from one

provider

Main aspects:

• Deliver an ABAP-based

implementation of the BW NLS

interfaces

• Deliver a Sybase IQ DBSL ‘light’ that

covers all the needs of the above-

mentioned NLS implementation

• Sybase IQ to deliver reliable, high-

performance execution of the DBSL

driven loads and queries

• Availability since Q2/2013 SAP owned alternative to

existing NLS-Partner Solutions


… SAP HANA Extended Table HANA SP07

RT

DP

HANA

IQ

Col/Row

Table

Extended

Table

IQ Table

HANA Studio / Applications / Clients

Additional table type: Extended Table

Alternative Storage in IQ

Similar compression rates

Optimized data transfer between HANA and IQ

Data Processing can be pushed to IQ

Monitoring in HANA Studio

Joint Backup&Recovery across HANA and IQ


Extended Tables in HANA BW Use Case: Staging and Corporate Memory

RT

DP

HANA

IQ

A Table

IQ Table

BW DataSources and write-optimized

DSOs can have the property

“Extended Table”

Generated Tables are of type

“Extended”

Write and Read operations are re-

directed to IQ

All BW standard operations

supported – no changes

Only minor temporary RAM required

in HANA

DataSource

PSA Table

IQ Table

DataSource DataSource

wo-DSO wo-DSO

wo-DSO

Corporate Memory Staging Area


Extended Tables in HANA BW Use Case: Nearline Storage

RT

DP

HANA

IQ

NLS

IQ Table

BW IQ Server can be used for NLS

archive

Optimized data transfer from HANA

to IQ – no application server round

trip

DataArchivingProcess manages the

data transfer

Optimized Query access – similar to

SmartDataAccess optimization

Updates into NLS partitions possible

at any time

InfoCube /

DSO

Data Archiving Process

Trigger Control

INSERT AS SELECT


Big Data: 2.5 PB in #BWonHANA

http://www.saphana.com/videos/1868


BW 7.4 Feature Overview and Platform Availability - I

Topic Category HANA only

Extension of max. char. Value Renovation

Extra-long text Renovation

XXL-Attributes Renovation

High-Cardinality InfoObject (SID-less InfoObject) Renovation

BW Modeling Tools in Eclipse (Composite Provider, Open ODS View… ) Metadata&Modeling X

CompositeProvider Metadata&Modeling X

HANA Model Generation for BW InfoProvider Metadata&Modeling X

InfoObjects based on Calculation View Metadata&Modeling X

Inventory Keyfigures for DSO, VirtualProvider, CompositeProvider Analytic Manager X

OLAP: Calculation push-down AnalyticManager X

OLAP: Stock coverage keyfigure AnalyticManager X

OLAP: FIX operator AnalyticManager

OLAP: Multi-dimensional FAGGR AnalyticManager

OLAP: Current Member AnalyticManager

PAK enhancements AnalyticManager X

Planning on local provider in BW Workspace AnalyticManager X

Planning function push-down AnalyticManager X

Planning: ODATA & Easy Query extensions AnalyticManager

Planning: Support on HANA views for facts and master data AnalyticManager X


BW 7.4 Feature Overview and Platform Availability - II

Topic Category HANA only

Open ODS Layer – Open ODS View EDW X

Support of Smart Data Access EDW X

HANA Analysis Process EDW X

Transformation based on HAPs (In-Memory Transformations) EDW X

Field-based DataStore Objects EDW X

Bulk load capabilities EDW

Open Hub: Push data into a connected database EDW Operational Data Provisioning - PSA becomes optional – renewed integration with SAP extractors and renewed BW data mart scenario EDW

Operational Data Provisioning - ODQ for SLT EDW

Operational Data Provisioning – Dataservices Integration EDW

Data request house keeping EDW DTP for Hierarchies: extract multiple hierarchies request by request from PSA into data target EDW

Monitoring integrated in DBA cockpit for Sybase IQ NLS

Optimized Query-access to NLS data in Sybase IQ leveraging SDA NLS

Support to archive InfoProviders containing non cumulative key figures NLS

BW Workspace enhancements: Data Cleansing Misc X

Re-Modeling Toolbox Enhancements Misc X

New WebDynpro-based Masterdata Value Maintenance Misc

HANA-optimized BW Business Content Misc X


SAP NetWeaver BW on SAP HANA Get your own system today

Get your very own SAP NetWeaver BW on

SAP HANA with SAP BI 4.1 system today !

The BW 7.4 on HANA + BI 4.1 Trial

http://www.saphana.com/community/blogs/blog/2014/02/06/announcing-the-bw-74-on-hana-bi-41-trial

http://www.saphana.com/community/blogs/blog/2014/02/06/announcing-the-bw-74-on-hana-bi-41-trial

http://www.saphana.com/docs/DOC-3954

Agenda



BW + HANA + IQ

Success Stories


SAP BW Today (Feb 2014)

14500+ Customers

Vast majority: Central EDW, harmonizing many source systems

Embedded into mission critical business processes

200 New Installations/Month

3500+ BW 7.3 Customers

850+ BW on HANA Customers


Success Story..

Oil Market analysis for forwards trading: Formerly they were only able to analyze a 3 day

window of trading. It takes 11.5 hours on 22 TB Oracle instance. Each day's data has to be

duplicated to get the "performance", which is why the instance is so big. On HANA they are

able to analyze a 60 day window in only 20 minutes and that database it is only 0.5 TB due to

no data duplication and compression. The ROI was completely simple justified just on HW

alone

With BW they used to spend 80% of their time report writing and only twenty percent analyzing

data. With BW on HANA it is entirely the other way around

“With BW on HANA, we haven’t had the need to make a new info-cube in over a year and a half.”


Thank you

Contact:

Dinesh [email protected]

Garimella Shashidhar [email protected]

SAP Labs India Pvt. Ltd.

mailto:[email protected]

mailto:[email protected]

Appendix

Unleashing Social Media Platform in Decision Making using SAP HANA

Abhinav Sharma

CSC India Pvt Ltd

Agenda

Introduction: Social Media – The power of Big Data

Challenges and Opportunities

SAP HANA – A platform for Big Data

Business Case Presentation

Summary and Key Takeaways

Why Social Data? Why Now?

Business is driven by set of challenges and set of questions to answer

Analytics is as good as the level of information, amount of information and

quality of information

Analyzing large volume of social data can be more effective and helps in

calculative decision making

No one wants to drive a car by looking into rear-view mirror.

Information must be available or readily available at the finger tips anytime

and anywhere

More and more data is being generated and now it becomes integral part

of life. ( Social Media )

Growth of Social Media – 2013

1. Mobile Phones increased from 60.3% to 818.4m in last two years

2. FB has 665m daily active users

3. Twitter is growing at 44% and monthly active users are 228m

4. Google+ growing at 33% and has 395m monthly active users

5. YouTube hours watched doubled

6. LinkedIn has active 200m users

7. In 2011, the amount of data surpassed 1.8 ZettaBytes

8. 46% of "business leaders“ planning to increase social media budgets

in 2014

The Challenge

Bring together a Large volume and Variety of Data to find New Insights

The Opportunity

Extracting Insights from an immense volume, variety and velocity of data,

in context, beyond that was previously possible

Velocity

Volume

Variety

Mobile

CRM Data

PlanningOpportunitiesTransactions

Customer

Sales Order

Things

Instant Messages

Demand

Inventory

:-)Brand

SentimentHigher NPS

360O Customer ViewLoyal Customers

Product Recommendation

More Sales

Propensity to Churn

Greater Retention

Real-time Demand/

Supply ForecastMore Efficient

Fraud Detection

Lower Risk

Risk Mitigation, Real-time

Retain Market Value

Asset TrackingIncrease

Productivity

Personalized CareLoyal

Customers

Decoding BIG DATA – Six V’s

New Approach

Business and IT identifyInformation Sources Available

IT delivers platform to exploreAvailable data and content

Business determines what questionsTo ask by exploring the data and relationships

New insights drive integration to Traditional technology

SAP HANA Platform – Big Data Approach

Different Types of Data

Data comes in many different shapes and sizes

Different Forms of DATA

Structured Data

Well-defined Content

Easily Understood

Stored in RDBMS

Unstructured Data

Not obvious structure

Process data to understand

Not suitable for RDBMS

Semi-Structured Data

Combines properties of both

Social Media falls under this category

Examples: Email, Social Media Feeds, Video feeds etc

Business Scenario

• Loading data from Twitter to SAP HANA System

• Text Analytics on Twitter data

Questions

SAP Business Objects powered by SAP HANA

Presenter Name : Saurabh Raheja

Company Name : Infosys

Agenda

Overview of SAP HANA

Overview of SAP Business Objects

SAP BO BI suite 4.1 on SAP HANA

Generation of Reports

Business Scenario

In any Business Enterprise:-

1) The volume of data goes on increasing continuously

2) Speed at which data increases is high.

3) Variety of data sources are used e.g. Flat Files, RDBMS, etc.

Traditional Database :- It is used to store data on Hard disk drive(HDD).

Approach :- Query was sent to database layer, executed and data is

returned to Application Layer. All the Logics and Calculations were

performed at application layer.

As a result, there was latency in read and write operations as every

database R/W operation involves a heavy cost.

There were separate OLTP and OLAP systems.

These were the major bottlenecks of any business enterprise.

Solution to all these problems is SAP HANA.

What is SAP HANA??

• High Performance Analytical Appliance

• SAP HANA is an In Memory Database. Entire database is stored in

Main Memory e.g. RAM.

• SAP HANA is real-time data platform.

• Leverages technology through row and columnar storage, massively

parallel processing and data compressions. This allow organizations

to instantly explore and analyze very large volume of transactional

and analytical data.

• Reads data in few seconds which used to take 1 hour with traditional

databases.

• Cost effective, better compression techniques and performance due

to I/O operations

• SAP HANA also has a persistence layer e.g. Solid State Drive(SSD),

Flash Drive, etc. so that if power goes off we still have a backup of

data.

Cntd…

SAP HANA follows push down approach. No logics are processed at

Application Layer. Calculations and logics are done at the database

layer and result is returned to Application Layer.

Cntd..

SAP HANA allows us to leverage the benefits of both storage approaches

OLTP and OLAP systems.

OLTP systems(Row storage)

• High amount of write and update operations.

• Typically complete record needs to be accessed.

• Processes only one record at a time.

• Allows reading large number of attributes against single key

OLAP systems(Column Storage)

• High amount of read operations.

• Calculations are performed on a single column or few columns.

SAP HANA Architecture

SAP HANA Performance Benchmarks

The test system configuration is a 16-node cluster of IBM X5 servers with 8TB of total RAM. Each server has:

4 CPUs with 10 cores and 2 hyper-threads per core, totaling

40 cores

80 hyper-threads

512 GB of RAM

3.3 TB of disk storage

Data compression occurs during the data loading process. HANA demonstrated a greater than 20X compression rate. The 100TB SD data set was reduced to a trim 3.78TB HANA database, consuming only 236GBs of RAM on each node in the cluster .

The Reporting and Drill-down queries took 267 milliseconds to 1.041 seconds. This demonstrate HANA’s excellent ability to aggregate data

Why SAP HANA with Analytics?

• Analytics that unleash the power of collective insight

• Using analytics tools to collect massive amounts of Big Data from your

organization is one thing. Extracting meaning from that data and using

it to drive real growth is another. Business analytics from SAP can help

you unleash the power of collective insight by delivering enterprise

business intelligence, agile visualizations, and advanced predictive

analytics to all users – on any device or platform.

• Make fact-based decisions throughout your organization by relying on

our business intelligence solutions. Easily access relevant information

when and wherever you need it to better understand your business, act

quickly and confidently and ultimately achieve remarkable results.

• Provide intuitive, self-service access to business information

• Enable informed and rapid decisions based on reliable and real-time

business data

• Maximize visibility into the performance of your business network

• Simplify deployment and optimized use of IT infrastructure and

resources

SAP Business Objects

For analyzing data in HANA, SAP offers the SAP Business objects

Business Intelligence suite of products. SAP Business Objects BI platform

4.1 is a suite of front end applications.

The suite includes the following key applications:

• Crystal Reports -- Enables users to design and generate reports. SAP

Crystal Report 2013 can connect directly to tables and views in HANA

to create formatted report.

• Dashboards -- Allows users to create interactive dashboards that

contain charts and graphs for visualizing data

• Web Intelligence -- Provides a self-service environment for

creating ad-hoc queries and analysis of data.

SAP Business Objects Web Intelligence and Dashboards use

relational Universes to connect to HANA to analyze data and create

reports and visualizations. The universe can be based on views and

tables in HANA.

Crystal Reporting

• Quickly create highly formatted, pixel-perfect reports

• Connect to data sources across your organization – directly or through

a common semantic layer

• Deliver operational reporting that can help you make day-to-day

business decisions

• Give personalized reports to users in their preferred language, format,

and delivery method

Web Intelligence Reporting

• Deliver personalized business intelligence to your colleagues,

customers, and partners

• Improve productivity by giving users an intuitive tool and clearing IT

backlogs

• Improve ad hoc reporting and analytics across any data source with a

flexible framework

• Get the insights you need, when you need them, no matter where you

are

What is Universe in Business Objects?

Universe is semantic layer (middleware) between database and end users.

Universe contains Objects that map to actual SQL structures in the

database such as columns, tables, and database functions.

Objects are grouped into classes.

Objects and classes are both visible to Web Intelligence users

A schema of the tables and joins used in the database.

Web Intelligence users connect to a universe, and run queries against a database. They can do data analysis and create reports using the objects in a universe without having to know anything about, the underlying data structures in the database

What is the role of a universe?

Easy to use and understand interface for non technical Web Intelligence users to run queries against a database to create reports and perform data analysis.

As the universe designer, you use Designer to create objects that represent database structures. The objects that you create in the universe must be relevant to the end user business environment and vocabulary. Their role is to present a business focused front end to the SQL structures in the database.

SAP Business Objects Information Design Tool

• In SAP BusinessObjects 4.0, one of the major changes is the new "Information Design Tool" . It is a replacement for the old Universe Design Tool.

• Using Information Design Tool, you can build universe(UNX) that are stored in SAP Business Objects BI platform repository. The universe do not store data themselves

• Relational universes can be built directly on tables or views in HANA..

.

SAP BO BI 4.1 on SAP HANA

• Earlier the world was running SAP Business Objects on Oracle, SQL

Server, or DB2 as the application layer databases. In SAP Business

Objects BI4 SP4, SAP introduced the ability to rest those databases on

SAP HANA.

• SAP HANA can be added to existing landscape which may already

include data sources and business intelligence software.

SAP HANA Studio

• SAP HANA Studio is the front-end software delivered with HANA

• Enables administration of HANA database and modelling of data in

HANA to create views.

• One can also use Information Design Tool (IDT) included in SAP

Business Objects business Intelligence 4.0 platform, to create

universe based on HANA data.

• Using the Information modeler perspective in SAP HANA studio , one

can create analytical and calculation views in HANA based on the

data in underlying tables.

• The views are logical structure intended to facilitate analysis of

important data in the underlying table. The view do not store data

themselves.

Views in HANA

Using SAP HANA

Three major steps of using HANA :-

1) Loading data into HANA from an existing data source.

2) Modelling the data in HANA to facilitate data analysis

3) Analyzing the data in HANA using Business Intelligence tools.

For loading data into HANA there are two methods :-

1) SAP Landscape Transformation(SLT)-Used to move data from an

SAP ERP database or any SAP supported database into SAP HANA.

The data replication is done in real time, so changes in the original

data source are immediately replicated to HANA

Cntd..

Data Services Transformation- SAP BODS 4.0 can be used to move data

from any data source to SAP HANA. Provides both data transformation

and data replication functionality. Data is scheduled in batches.

Generation of Reports:-

• When setting up a universe that connects to SAP HANA, you must first

create a relational connection to the HANA database using JDBC or

ODBC drivers

• Once you have created a relational connection to SAP HANA, the next

step is to create the data foundation for the universe. When

connecting to SAP HANA, you can build your data foundation by

selecting the appropriate tables and creating joins between them, or

you can build your data foundation directly on a pre-existing analytic

or calculation view.

• Once you have built your data foundation on an SAP HANA view, you

can finish your universe by creating a business layer that specifies

the folders, dimensions, and measures that will be available to users

when they connect to the universe using one of the client tools.

Cntd..

• You can build a report directly on SAP HANA by creating a connection

using ODBC or JDBC drivers

• you will create an ODBC System Data Source Name (DSN) for SAP

HANA.

• Using the 32 bit ODBC Data Source Administrator, add a new system

DSN.

• Select the appropriate driver for SAP HANA.

• HDBODBC32 driver is automatically installed with SAP HANA client.

• Define the unique name and the server and port combination for data

source and then test the connection.

• Enter the credentials for the connection. You have successfully

connected to SAP HANA.

Cntd…

• Crystal Report can be generated by connecting directly to views or

underlying tables in SAP HANA.

• In crystal report 2013 create a blank report and choose the new ODBC

DSN as your data source.

• Select the newly created data source name.

• Note that the fields from the SAP HANA tables are available to be

added in your report.

Questions

SAP HANA – Goal and Impact

Kumar Mayuresh

The Principal Consulting

Agenda

New Challenge of Data

SAP HANA Overview

Goal of SAP HANA DB

Impact of Modern Hardware on Database System Architecture

SAP HANA DB Tables

Memory Sizing

SAP HANA: Persistence Layer

Backup

Disaster Recovery

SAP HANA Approach to Business Needs

Application on HANA Landscape Impact

Application Based on SAP HANA Database

Delivery of SAP HANA

Q&A

New challenges with the data

Velocity

Volume Variety

Mobile

CRM Data

PlanningOpportunitiesTransactions

Customer

Sales Order

Things

Instant Messages

Demand

Inventory

:-)Brand Sentiment

Higher NPS

360O Customer View

Loyal Customers

Product Recommendation

More Sales

Propensity to Churn

Greater Retention

Real-time Demand/

Supply Forecast

More Efficient

Fraud Detection

Lower Risk

Risk Mitigation, Real-time

Retain Market Value

Asset Tracking

Increase Productivity

Personalized Care

Loyal Customers

SAP HANA Overview

Goal of SAP HANA DB

Executing Application Logic inside the Data Layer

Enabling New Types of Applications

High Performance and Scalability

Hybrid Data Management System

Compatibility and Standard DBMS Features

Support For Text Analysis, Indexing and Search

Multi-Tenancy

Impact of Modern Hardware on Database System

Architecture

The focus was on optimizing disk access, for example by minimizing number

of disk pages to be read into main memory when processing a query – But

today the performance bottleneck is now between the CPU cache and main

memory

Core

CPU

Performance bottleneck today:

CPU waiting for data to be

loaded from memory into cache

Performance bottleneck in the past: Disk I/O

Disk

CPU Cache

Main Memory

Characteristics of high performance data management system:-

In-memory database

•All relevant data must be kept in main memory, so read operations can be executed without disk I/O.Disk based index structures, for example, are not needed any more for an in-memory database. Diskstorage is still needed to make changes durable, but the required disk write operations happenasynchronously in the background.

Cache aware memory organization, optimization and

execution:

•The design must minimize the number of CPU cache misses and avoid CPU stalls because of memoryaccess. One approach for achieving this goal is using column-based storage in memory. Searchoperations or operations on one column can be implemented as loops on data stored in contiguousmemory arrays. This leads to high spatial locality of data and instructions, so the operations can beexecuted completely in the CPU cache without costly random memory accesses.

Support for parallel execution:

• In recent years CPUs did not become faster by increasing clock rates. Instead the number of processorcores was increased. Software must make use of multi-core processors by allowing parallel executionand with architectures that scale well with the number of cores. For data management systems thismeans that it must be possible to partition data in sections for which the calculations can be executedin parallel. To ensure scalability, sequential processing – for example enforced by locking – must beavoided wherever possible.

SAP HANA DB Tables : ROW Tables

Facts Interfaced from the Calculation/ Execution layer In-Memory store and persistence is managed in

the persistence layer Stores and retrieves data in rows, much like a

traditional relational database, except that the data is stored in memory

SAP HANA DB Tables : COLUMN Tables

• Facts• Interfaced from the Calculation/Execution layer• In-Memory store and persistence is managed in the

persistence layer• Optimized for read with efficient data compression

• Columnar Data Store Advantages• Optimized for reads• High data compression• Very fast data aggregation• Can be joined with row-based data

Memory Sizing

Source : SAP

Used Memory is the total amount of memorycurrently in use by SAP HANA. This is the mostprecise indicator of the amount of memory thatSAP HANA requires at any time.Resident memory is the physical memoryactually in operational use by a process• By default, Row store tables are loaded into

memory once HANA database starts• Column store tables are loaded into memory

when there is a query against that table• Column store table can be partially loaded

into the memory, that is, one or two selected columns can be loaded into memory

• Column store table can also be fully loaded into the table, depending on the query

Memory Sizing: Static Data

• Memory requirements for static data are derived from the database footprint of the

correspondingtables of the source system’s database system

• Database footprint in the source system must be determined using database-specific catalog

information (e.g., in Oracle: dba_segments; in DB2: syscat.tables)

• Database-specific scripts and more details on how to determine the database footprint can be found in

SAP Note 1514966

• Average compression factor in HANA memory = 7 : 1

• Note that this compression factor refers to uncompressed database tables and space for database

indexes is to be executed

RAMSTATIC = Source data footprint / 7 * c

Memory Sizing: Runtime Objects

• Additional memory is required for objects that are created dynamically

• When loading new data

• When executing queries

• Recommended to reserve as much memory for dynamic objects as for

static objects

RAMDynamic = RAMStatic

Total RAM is:

RAM = RAMDynamic + RAMStatic

= Source data footprint * 2 / 7 * c

Disk Sizing

Disk size for persistence layer. This does not cover backup space.

Diskpersistence = 4 * RAM

Disk size for log files/operational disk space

Disklog = 1 * RAM

Example

Source : SAP

SAP HANA: Persistence Layer

How Data from HANA Memory Is Written to Disk

Source : SAP

Backup

Source : SAP

Backups to file systemSAP Note 1651055 – Scheduling SAP HANA Database Backups in Linux

Backups to third-party backup toolsSAP Note 1730932 – Using backup tools with Backint for HANA

Disaster Recovery

Storage Replication only supports synchronous (for shorter distance); however, system replication supports asynchronous over longer distance

Disaster Recovery (contd)

Storage-based mirroring of SAP HANA disk areas controlled by storage technology

• Synchronous implementation

• Asynchronous implementation

WARM standby: DATA and LOG content is continuously transferred to secondary site under control

of SAP HANA database

• Fast switch-over times because secondary site has preloaded DATA



HOT Standby: DATA content is only initially transferred to secondary site; afterwards, continuous

LOG transfer and LOG replay on secondary site

• LOG is provided to secondary site on transactional basis (COMMIT) controlled by SAP HANA

database (including initial DATA transfer)

• Fastest switch-over times, sec. site preloaded and rolled forward on COMMIT basis



Disaster Recovery : Storage Replication

• Each node in Multi-Node HANA server comes with 512 GB memory• Benefits: Continuous replication of all persisted data, offers a more attractive RPO than backups• Limitation: Storage-based replication is only synchronous and limited to 100 KM. Requires a reliable high• bandwidth and low latency connection between the primary site and the secondary site.• Cost of implementation: Medium to high, depending on the exact business requirements and storage vendor

Disaster Recovery : System Replication

• Each node in Multi-Node HANA server comes with 512 GB Memory• If the DR distance is more than 100 KM, asynchronous system replication can be set up, but the RPO will be higher then synchronous

replication. Storage-based replication is synchronous and only supports DR up to 100 KM. Hence, HANA kernel-based system replication is the recommended

• option.• Benefits: Low to medium RPO and RTO depending on business requirement• Cost of implementation: Medium to high, depending on the exact business requirements

Disaster Recovery : TDI System Replication

• In this recommendation, Tailored Datacentre Integration HANA server is used. Existing certified SAN Storage can be used and only the HANA server without storage needs to be procured. Using TDI approach brings down the cost of ownership.

• Moving forward, SAP is planning to relax the network and allow users to leverage existing network infrastructure, which brings down the cost even more.

• Same 512 GB HANA can be used for both CDP (128 GB) and ERP (256 GB)• Benefits: Low to Medium RPO and RTO, depending on business requirements• Cost of implementation: Low to Medium, depending on the business requirements

SAP HANA Approach to Business Needs

• Focus: SAP ERP Acceleration – HANA used as an appliance

Data Acquisition: SAP ERP, SAP BW, and any non-SAP System.

• HANA used as a separate Appliance

• Can acquire real-time data from SAP ERP

• Can acquire data from any system as an ETL process

• Has the tools for complete lifecycle-like modelling, security, etc.

HANA (Native HANA)

• Focus: HANA as a database for painful transactional processing

• Data Acquisition: SAP ERP, SAP Business Suite

• HANA used as a database to host data from SAP Business Suite

• Can still acquire data from SAP ERP as real-time and via ETL from other sources

• ABAP can be leveraged to accelerate performance of “pain-areas

HANA (HANA Sidecar)

• Focus: HANA as a database for SAP BW, SAP BPC, SAP ERP

• Data Acquisition: SAP ERP, SAP BW, and any non-SAP system

• As a database to host SAP BW, SAP ERP …

• Acquire data from sources leveraging ETL/other processes, security, etc.

HANA (Application on HANA)

• Focus: HANA as a database for all SAP products

• Data Acquisition: SAP ERP, SAP BW, and any non-SAP system

• HANA as a database to host applications

• Acquire data from any non-SAP source

• Applications can be made leveraging HANA DB

HANA (Beyond SAP)

Application on HANA Landscape Impact

SAP HANA as Application Platform

Application Based on SAP HANA Database

Applications in the category “SAP HANA Applications” have theproperty of utilizing the SAP HANA database as their primarydatabase management system. Application logic is executedon the application server and SAP HANA database layer. TheSAP HANA database executes those parts of the applicationlogic that are data intensive and performance critical.

Architecture Overview of SAP HANA Applications

Different platforms are used for the application server layer of SAP HANA applications. For example:• The Next Generation ABAP Platform is used as the platform

for transactional and analytical applications, such as SAP Business by Design and High Performance Applications (e.g. Customer Analytics, Liquidity Risk Management).

• The SAP HANA based SAP NetWeaver Business Warehouse (BW) 7.30 SP5 is used as the platform for analytical SAP HANA applications such as Demand Signal Management.

• Some SAP HANA based Enterprise Performance management (EPM) applications use the Lean Java Server.

Delivery of SAP HANA

7 Key Points to Take Home

• SAP HANA is a platform and not just a database• SAP HANA database is based on In-Memory technology• While migrating SAP Business Suite and BW on HANA, a separate app server is required• SAP HANA provides the opportunity to unify transactional and analytical processing on the same

system• SAP HANA helps accelerate the business processes and provides real-time analytics capabilities• SAP HANA leads to smaller data footprint due to high compression• SAP HANA reduces TCO of overall landscape

Questions